We watched transfixed as a computer bested the two all-time champions on the game-show Jeopardy! The computer Watson provided three nights of geeky entertainment but honestly, what was Watson's purpose? Why was the machine built? Some of us even thought "big deal." After all, many of the word puzzles Watson solved we could have answered almost as quickly by typing the questions into a search engine.

Actually, Watson's triumph over human competitors is a very big deal. There's very complex technology going on in Watson's processing that surpasses traditional search algorithm technology. Watson provides IBM researchers with a way to delve into and in fact revamp data analysis as we know it. Watson's existence and the fascinating research in which it is currently involved will affect each and every one of us: how physicians diagnose us, how cities respond to natural disasters, how we decrease our carbon footprint, how we invest money, and much more.

Several IBM scientists at the IBM Thomas J. Watson Research Center in New York shared details of their current work, and each pointed out that Watson's capabilities drive much of their research.

Watson: the Evolution of Analytics
Dr. Chid Apte, IBM's director of Analytics Research, said Watson is an evolution from traditional data analysis. We are moving beyond the "first generation of analytics," which is the relational database, and working on predictive and descriptive analytics. Such data intelligence allows businesses to estimate what will happen in the future or next quarter.

"We are in an age of 'high frequency' data," Apte said. "Information is coming at us so fast there isn't even time to store it. Businesses with billions of transactions need massive scale analyticsWall Street, retail, healthcare, and so on."

With Watson, IBM researchers had the analytics all figured out, but it initially took as long as one day for Watson to process all the information in its data store to come up with an answer. To play Jeopardy!, the computer had to come up with an answer in less than three seconds. So, scientists kept adding processors. It took a total of 90 servers to achieve that fast turnaround.

These analytical capabilities are applicable to what IBM deems "Watson-like technologies" that expand into many of the research projects at IBM. One is the Smarter Planet, Smarter Cities initiatives. These are projects focused on using data and analytics to make a more sustainable planet, to help cities manage energy outages, emergencies, traffic patterns, and predict the impact of natural disasters on an area.

Jurij Paraszczak is director of industry solutions and Smarter Cities at IBM and Lloyd Treinish is the chief scientist for Deep Thunder, a service that provides local, high-resolution weather predictions customized to business applications. Both are at the helm of the IBM's Smarter Planet, Smarter Cities projects, which use analytic technology similar to Watson's. In fact, Deep Thunder is being used by the city of Rio de Janeironotorious for floodingto predict not only weather but also the likelihood of damage. According to Treininsh, Deep Thunder is not solely about predicting weather but to "reduce uncertainty," especially when determining how much damage a natural disaster could cause.

Watson in Real-World Applications
Watson is well-suited for any application where there is a vast amount of data and people need access to it quickly. But, how does it get from game show contestant to real-world application?

Jennifer Chu-Carroll is one of the scientists who helped develop Watson. She said that perhaps the biggest misconception about Watson is that it is using the same search technology as Google or Bing. She said many people don't "appreciate the difference between typing in two keywords, getting back 100,000 results, and then having to go through those results to find relevant information. Watson can answer a question given in natural language and have a confidence level associated with that answer, she said.

Watson Deconstructed
Is Watson considered artificial intelligence? Chu-Carroll said it depends on your definition of AI. If you define AI as mimicking the human brain, then the answer is no. She said researchers take a more pragmatic approach to AIWatson does not come up with answers the same way as humans. We make inferences, guesses, and understand the nuances of language better than Watson.

However, Watson is getting better at understanding the subtleties of language. It is able to process some puns and understand that language in Jeopardy! is can be used evoke some meanings other than the obvious. As we witnessed, Watson can play the game as well as the best players. Chu-Carroll said, "People say, 'Well, airplanes don't fly the same as birds do.' Do you think airplanes are flying?"

Watson is a combination of hardware with a lot of processing power and IBM's DeepQA analytic technology. One could run Watsonthe softwareon a laptop, but it would run very slow. Since the Jeopardy tournament, Chu-Carroll and other members of the Watson team have been customizing Watson's analytics for the healthcare industry, with a goal of using the technology to help diagnose patients.

Watson has still limitations. "Watson right now is a system that is very static as far as taking information in and giving an answer," said Chu-Carroll. "Watson actually accesses a lot of information while processing the questions. It's trying to find the information it's most sure about, but there may be other information that is found that it's not so sure about. We are trying to see how we can leverage human intelligence to help Watson know either that information is not relevant or that this is actually additional information that can help improve the confidence of an answer. We are trying to figure out how we can interact with users to leverage human intelligence to help Watson be even smarter."

If you have ever been in any enterprise data center, you will have an idea of what Watson "looks" like, up close and personal. Ninety IBM Power 750 servers are rack-mounted, each connected to one another by a neatly assembled series of fiber and Ethernet cables. Watson remains offline, only connected to IBM's internal network. It's a perfect model of a parallel computing systemeach server holds data and uses its processing power to make the entire systemor Watsonrun fast and efficient.

Watson is, after all, a system comprising software and hardware. It seems reasonable that someone would contact IBM and say, "I want to buy a Watson." Chu-Carroll laughed and said, "People are doing that." She pointed out that the software takes some work to fit into specific industries, like healthcare and finance.

For some, watching a computer beat a human in a game show evoked the idea of a supercomputer; perhaps one day turning against mankind, our fears of a Skynet realized. Chu-Carroll dismissed the idea. "The program is developed to accept information to help users make better decisions." She said that Watson and humans can have a beneficial relationship. "The computer helps a person by doing things it's better at, so that a person has more time to do what [he or she] is better at."

Samara Lynn has nearly twenty years experience in Information Technology; most recently as IT Director at a major New York City healthcare facility. She has a Bachelor's degree from Brooklyn College, several technology certifications, and she was a tech editor for the CRN Test Center.
With an extensive, hands-on background in deploying and managing Microsoft Windows infrastructures and networking, she was included in Black Enterprise's "20 Black Women in Tech You Need to Follow on Twitter," and received the 2013 Small Business Influencer Top 100 Champions...
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